Publications

Journal Articles

CFM-UNet: coupling local and global feature extraction networks for medical image segmentation

Published in Scientific Reports, 2025

CFM-UNet proposed integrates CNN-based Bottle2neck blocks for local feature extraction and Mamba-based visual state space blocks for global feature extraction. These parallel frameworks perform feature fusion through our designed SEF block, achieving complementary advantages

Recommended citation: Niu, K., Han, J. & Cai, J. CFM-UNet: coupling local and global feature extraction networks for medical image segmentation. Sci Rep 15, 22236 (2025). https://doi.org/10.1038/s41598-025-92010-y
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Conference Papers

GM-UNet: GAN-Based VM-UNet Achieves Higher Accuracy

Published in 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024

GM-UNet proposed enhances VM-UNet with a GAN framework and adversarial loss, achieving superior liver CT segmentation performance by better capturing image details.

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PESAM: Privacy-Enhanced Segment Anything Model for Medical Image Segmentation

Published in Advanced Intelligent Computing Technology and Applications (ICIC 2024), 2024

PESAM proposed integrates federated learning with SAM to enable collaborative optimization of medical image segmentation models while preserving data privacy, using a dynamic local aggregation algorithm to enhance performance and generalizability across heterogeneous data.